Abstract

Machine learning (ML) methods are becoming increasingly popular in the identification of biomarkers in medical imaging data. While these methods provide superior predictive accuracy, their models can be hard to interpret. More conventional statistical methods lack the predictive accuracy of the ML models, but are able to identify regions in which the difference between a patient group and healthy controls is statistically significant. We attempted to strike a middle ground between these two extremes by employing supervised dimensionality reduction (SDR) methods in the identification of useful discriminative patterns for the differential diagnosis of various parkinsonisms based on FDG-PET scans. Additionally, a new SDR method based on margin maximization in a lower dimensional space has been developed. Results indicate that our method performs on-par with existing methods in terms of accuracy, while at the same time providing an intelligible model through the generation of features that improve to performance of a radial kernel support vector machine. In addition, we are able to demonstrate that our method is able to learn an efficient low-dimensional representation of high-dimensional data.